Digital Terrain Models (DTMs) represent the bare-earth elevation and are important in numerous geospatial applications. Such data models cannot be directly measured by sensors and are typically generated from Digital Surface Models (DSMs) derived from LiDAR or photogrammetry. Traditional filtering approaches rely on manually tuned parameters, while learning-based methods require well-designed architectures, often combined with post-processing. To address these challenges, we introduce Ground Diffusion (GrounDiff), the first diffusion-based framework that iteratively removes non-ground structures by formulating the problem as a denoising task. We incorporate a gated design with confidence-guided generation that enables selective filtering. To increase scalability, we further propose Prior-Guided Stitching (PrioStitch), which employs a downsampled global prior automatically generated using GrounDiff to guide local high-resolution predictions. We evaluate our method on the DSM-to-DTM translation task across diverse datasets, showing that GrounDiff consistently outperforms deep learning-based state-of-the-art methods, reducing RMSE by up to 93% on ALS2DTM and up to 47% on USGS benchmarks. In the task of road reconstruction, which requires both high precision and smoothness, our method achieves up to 81% lower distance error compared to specialized techniques on the GeRoD benchmark, while maintaining competitive surface smoothness using only DSM inputs, without task-specific optimization. Our variant for road reconstruction, GrounDiff+, is specifically designed to produce even smoother surfaces, further surpassing state-of-the-art methods. The project page is available at https://deepscenario.github.io/GrounDiff/.

翻译：数字地形模型（DTM）表征了裸地高程，在众多地理空间应用中具有重要意义。此类数据模型无法通过传感器直接测量，通常需从激光雷达或摄影测量衍生的数字表面模型（DSM）中生成。传统滤波方法依赖人工调参，而基于学习的方法需要精心设计的架构，并常需结合后处理。为应对这些挑战，我们提出了Ground Diffusion（GrounDiff），这是首个基于扩散模型的框架，通过将问题构建为去噪任务来迭代消除非地面结构。我们引入了带置信度引导生成的门控设计，以实现选择性滤波。为提升可扩展性，我们进一步提出先验引导拼接（PrioStitch），该方法利用GrounDiff自动生成的下采样全局先验来指导局部高分辨率预测。我们在多源数据集上对DSM到DTM的转换任务进行了评估，结果表明GrounDiff在ALS2DTM数据集上降低RMSE达93%，在USGS基准测试中降低达47%，持续优于基于深度学习的先进方法。在需要高精度与平滑度的道路重建任务中，本方法在GeRoD基准测试中比专用技术的距离误差降低达81%，且仅使用DSM输入（无需任务特定优化）即保持了具有竞争力的表面平滑度。我们针对道路重建的变体GrounDiff+专门设计用于生成更平滑的表面，进一步超越了现有先进方法。项目页面详见 https://deepscenario.github.io/GrounDiff/。